@article{ref1,
title="Non-response models for the analysis of non-monotone ignorable missing data",
journal="Statistics in Medicine",
year="1997",
author="Robins, J. M. and Gill, R. D.",
volume="16",
number="1-3",
pages="39-56",
abstract="We discuss a new class of ignorable non-monotone missing data models-the randomized monotone missingness (RMM) models. We argue that the RMM models represent the most general plausible physical mechanism for generating non-monotone ignorable data. We show that there exists ignorable missing data processes that are not RMM. We argue that it may therefore be inappropriate to analyse non-monotone missing data under the assumption that the missingness mechanism is ignorable, if a statistical test has rejected the hypothesis that the missing data process is RMM representable. We use RMM models to analyse data from a case-control study of the effects of radiation on breast cancer.<p /><p>Language: en</p>",
language="en",
issn="0277-6715",
doi="",
url="http://dx.doi.org/"
}